TFM2_16S_amplicons_analysis
lrubinat
5/30/2017
1) 16S amplicons
1.1) Data overview
The dataset “Taraspina 18S miTags” contains reads from 122 samples of Malaspina. On average, each sample contains 45940 OTUs:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 405 20410 33100 45940 68340 148200
Overall reads per sample:
1.2) Normalization
In order to keep as many samples as possible, we rarefy at 16509 reads per sample. By that, we loose 31 samples, and after removing the exluded samples in the 18S dataset (to make them comparable), we end up with a normalized dataset containing 91 samples and 8881 OTUs.
Datasets summary:
dim(tb16_tax) #original dataset## [1] 9114 128dim(tb16_tax_occur) #original dataset with occurrence data alone## [1] 9114 122dim(tb16_tax_occur_min16509) #dataset without samples with less than 16509 OTUs## [1] 8881 91dim(tb16_tax_occur_ss16509_no_cero) #rarefied dataset## [1] 91 81731.3) General community analysis
1.3.1) Richness and evenness (Shannon index)
Most of the samples take Shannon Index values between 2.5 and 3.5:
1.3.2) Richness: OTU number
Lowest number of OTUs per sample:
## [1] 618Maximum number of OTUs per sample:
## [1] 1660In most of the samples, we can identify about 1400 OTUs:
1.3.3) Index of evenness
1.3.3.1) Pielou’s index
The Pielou index (constrained between 0 and 1) takes values closer to 1 as the variation of species proportion in a sample increases. Our samples get values around 0.6, meaning that the numerical composition of different OTUs in a sample is not so variable - we might observe certain dominant species.
1.3.4) Abundance Models
Most of the OTUs show very few occurrences, suggesting that we will probably be able to identify a significant ammount of rare otus:
1.3.4.1) Rank-Abundance or Dominance/Diversity Model (“radfit”)
The OTUs abundance distribution fits relativelly close to log-normal model.
1.3.4.2) Preston’s Lognormal Model
According to Preston’s lognormal model fit into species frequencies groups, we’re missing ~262 species:
veiledspec(tb16_tax_occur_ss16509_prestonfit)## Extrapolated Observed Veiled
## 9143.6079 8881.0000 262.6079
When computing Prestons’ lognormal model fit without pooling data into groups, we miss ~251 species:
## Extrapolated Observed Veiled
## 9132.6917 8881.0000 251.69171.3.5) Rarefaction curves of rarefied and non-rarefied datasets
1.3.6) Beta diversity
1.3.6.1) Dissimilarity matrix using Bray-Curtis index:
The Bray-Curtis dissimilarity, constrained between 0 (minimum distance) and 1 (highest dissimilarity) allows us to quantify the differences between samples according to the composition and relative abundance of their OTUs. In our dataset, most of the samples pairs take dissimilarity values between between 2 and 4, meaning that their composition is substantially similar.
1.3.6.2) Hierarchical clustering
The stations seem to form clusters according to geographic localization, but there are no evident clusters separated from the general groups.
(To be done: assign Longhurst provinces information to each station and check if any of the central clusters is meaningful regarding to the samples’ geographical location)
1.3.6.3) Non-metric multidimensional scaling
We can identify a prominent group in the central part of the NMDS plot and a few outliers in the central-high edge of the plot. The stress parameter takes a value below 0.2, suggesting that the plot is acceptable.
##
## Call:
## monoMDS(dist = tb16_tax_occur_ss16509_no_cero.bray)
##
## Non-metric Multidimensional Scaling
##
## 91 points, dissimilarity 'bray', call 'vegdist(x = tb16_tax_occur_ss16509_no_cero, method = "bray")'
##
## Dimensions: 2
## Stress: 0.1477793
## Stress type 1, weak ties
## Scores scaled to unit root mean square, rotated to principal components
## Stopped after 66 iterations: Stress nearly unchanged (ratio > sratmax)
When implementing a most robut function for computing NMDS plots, the result is quiet the same:
## Run 0 stress 0.117853
## Run 1 stress 0.1353845
## Run 2 stress 0.1526092
## Run 3 stress 0.1274873
## Run 4 stress 0.1457466
## Run 5 stress 0.1195488
## Run 6 stress 0.1561102
## Run 7 stress 0.1195447
## Run 8 stress 0.1484464
## Run 9 stress 0.1262914
## Run 10 stress 0.1240175
## Run 11 stress 0.1274872
## Run 12 stress 0.1314554
## Run 13 stress 0.1335539
## Run 14 stress 0.1540295
## Run 15 stress 0.1316594
## Run 16 stress 0.1309888
## Run 17 stress 0.1562589
## Run 18 stress 0.1218611
## Run 19 stress 0.1436726
## Run 20 stress 0.1569773
## *** No convergence -- monoMDS stopping criteria:
## 19: stress ratio > sratmax
## 1: scale factor of the gradient < sfgrmin## Warning in ordiplot(x, choices = choices, type = type, display = display, :
## Species scores not available
1.4) Geographical analysis
Communities quickly change their composition across geographical distances:
1.4.1) Mantel correlograms
Mantel statistic is -significantlly- so low, meaning that the correlation between samples dissimilarity and geographical distances is weak.
##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = geo_distances_MP_18S, ydis = tb16_tax_occur_ss16509_no_cero.bray)
##
## Mantel statistic r: 0.1083
## Significance: 0.001
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0200 0.0291 0.0359 0.0467
## Permutation: free
## Number of permutations: 999Correlograms:
MP_18s_ss16509_mantel_correl_by_1000km<-mantel.correlog(tb16_tax_occur_ss16509_no_cero.bray, D.geo=geo_distances_MP_18S, break.pts=seq(0,20000, by=1000))
plot(MP_18s_ss16509_mantel_correl_by_1000km)
MP_18s_ss16509_mantel_correl_by_100km<-mantel.correlog(tb16_tax_occur_ss16509_no_cero.bray, D.geo=geo_distances_MP_18S, break.pts=seq(0,20000, by=100))
plot(MP_18s_ss16509_mantel_correl_by_100km)
1.5) Abundance vs. occurence
OTUs distribution according to their percentage of occurence and relative abundance.
- red line: OTUs that occur in more than 80% of the samples.
- blue line: regionally abundant OTUs (> 0.1%).
- green line: regionally rare (< 0.001%).
Regionally abundant OTUs (relative abundance over 0.1%):
## otu_names mean_rabund perc_occur SILVA_consensus
## 1 OTU_1 0.272434150 100.00000 <NA>
## 19 OTU_2 0.079469141 100.00000 <NA>
## 32 OTU_3 0.039445684 100.00000 <NA>
## 60 OTU_5 0.019958478 97.80220 <NA>
## 74 OTU_6 0.017176112 100.00000 <NA>
## 43 OTU_3619 0.017119533 100.00000 <NA>
## 91 OTU_8 0.013883869 100.00000 <NA>
## 102 OTU_9 0.010413234 100.00000 <NA>
## 48 OTU_4 0.009755585 89.01099 <NA>
## 85 OTU_7 0.009328911 100.00000 <NA>
## 62 OTU_51 0.007644182 100.00000 <NA>
## 77 OTU_62 0.006768869 100.00000 <NA>
## 8 OTU_14 0.006130522 100.00000 <NA>
## 7 OTU_13 0.005621309 100.00000 <NA>
## 42 OTU_36 0.005459560 48.35165 <NA>
## 4 OTU_11 0.005357051 100.00000 <NA>
## 51 OTU_43 0.005245224 98.90110 <NA>
## 63 OTU_52 0.005070162 100.00000 <NA>
## 21 OTU_203 0.004783272 81.31868 <NA>
## 88 OTU_75 0.004463766 90.10989 <NA>
## 2 OTU_10 0.004287372 96.70330 <NA>
## 10 OTU_16 0.004263409 94.50549 <NA>
## 9 OTU_15 0.003885327 100.00000 <NA>
## 6 OTU_12 0.003390092 96.70330 <NA>
## 5 OTU_112 0.003313544 84.61538 <NA>
## 75 OTU_6052 0.003268946 84.61538 <NA>
## 28 OTU_27 0.003186407 83.51648 <NA>
## 24 OTU_23 0.003173094 96.70330 <NA>
## 44 OTU_38 0.003160447 100.00000 <NA>
## 97 OTU_8518 0.003129162 86.81319 <NA>
## 40 OTU_35 0.003090555 91.20879 <NA>
## 16 OTU_18 0.002965415 97.80220 <NA>
## 13 OTU_17 0.002817644 97.80220 <NA>
## 84 OTU_6983 0.002793681 82.41758 <NA>
## 33 OTU_30 0.002775709 100.00000 <NA>
## 37 OTU_33 0.002741761 85.71429 <NA>
## 26 OTU_26 0.002640584 97.80220 <NA>
## 29 OTU_2754 0.002637256 98.90110 <NA>
## 25 OTU_24 0.002628603 96.70330 <NA>
## 18 OTU_19 0.002609965 95.60440 <NA>
## 39 OTU_34 0.002569361 93.40659 <NA>
## 22 OTU_21 0.002515444 60.43956 <NA>
## 66 OTU_5345 0.002511451 87.91209 <NA>
## 53 OTU_45 0.002384314 91.20879 <NA>
## 72 OTU_5713 0.002192610 87.91209 <NA>
## 98 OTU_86 0.002167982 97.80220 <NA>
## 34 OTU_303 0.002099421 100.00000 <NA>
## 90 OTU_77 0.002020876 76.92308 <NA>
## 35 OTU_31 0.002017548 91.20879 <NA>
## 78 OTU_6249 0.001911046 100.00000 <NA>
## 52 OTU_44 0.001904389 94.50549 <NA>
## 93 OTU_8015 0.001901061 100.00000 <NA>
## 99 OTU_8731 0.001886417 91.20879 <NA>
## 38 OTU_3305 0.001879095 82.41758 <NA>
## 11 OTU_1666 0.001863785 91.20879 <NA>
## 47 OTU_3997 0.001828506 86.81319 <NA>
## 20 OTU_20 0.001815859 79.12088 <NA>
## 71 OTU_57 0.001789899 100.00000 <NA>
## 31 OTU_29 0.001787237 94.50549 <NA>
## 15 OTU_178 0.001763940 98.90110 <NA>
## 45 OTU_39 0.001755286 78.02198 <NA>
## 30 OTU_28 0.001753955 71.42857 <NA>
## 101 OTU_8904 0.001714017 100.00000 <NA>
## 55 OTU_47 0.001692716 96.70330 <NA>
## 17 OTU_182 0.001624821 95.60440 <NA>
## 65 OTU_53 0.001615502 69.23077 <NA>
## 41 OTU_350 0.001594202 96.70330 <NA>
## 12 OTU_167 0.001550936 78.02198 <NA>
## 50 OTU_42 0.001534960 83.51648 <NA>
## 56 OTU_48 0.001467065 82.41758 <NA>
## 54 OTU_46 0.001455749 86.81319 <NA>
## 27 OTU_269 0.001453752 68.13187 <NA>
## 64 OTU_5214 0.001422468 98.90110 <NA>
## 49 OTU_40 0.001399170 85.71429 <NA>
## 103 OTU_9607 0.001385192 96.70330 <NA>
## 96 OTU_8415 0.001375873 100.00000 <NA>
## 86 OTU_72 0.001367885 95.60440 <NA>
## 61 OTU_5092 0.001357235 82.41758 <NA>
## 95 OTU_836 0.001337932 95.60440 <NA>
## 14 OTU_170 0.001290006 85.71429 <NA>
## 70 OTU_5677 0.001268705 98.90110 <NA>
## 87 OTU_73 0.001235423 19.78022 <NA>
## 3 OTU_101 0.001216786 69.23077 <NA>
## 67 OTU_54 0.001211460 91.20879 <NA>
## 58 OTU_49 0.001192823 89.01099 <NA>
## 68 OTU_55 0.001185501 60.43956 <NA>
## 46 OTU_396 0.001164866 100.00000 <NA>
## 69 OTU_5631 0.001161538 17.58242 <NA>
## 80 OTU_63 0.001151553 74.72527 <NA>
## 76 OTU_61 0.001148890 100.00000 <NA>
## 59 OTU_497 0.001148890 32.96703 <NA>
## 81 OTU_65 0.001136243 71.42857 <NA>
## 36 OTU_32 0.001124262 74.72527 <NA>
## 83 OTU_69 0.001121599 51.64835 <NA>
## 94 OTU_8316 0.001117606 89.01099 <NA>
## 23 OTU_22 0.001110284 87.91209 <NA>
## 89 OTU_7628 0.001098302 52.74725 <NA>
## 57 OTU_4850 0.001060361 90.10989 <NA>
## 73 OTU_59 0.001039060 91.20879 <NA>
## 92 OTU_80 0.001033070 41.75824 <NA>
## 79 OTU_627 0.001025082 98.90110 <NA>
## 100 OTU_89 0.001016429 85.71429 <NA>
## 82 OTU_6798 0.001002450 87.91209 <NA>
## SILVA_classif
## 1 KC002097.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_unidentified_marine_bacterioplankton
## 19 KM520635.1.1287_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 32 AACY020285848.922.2246_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_marine_metagenome
## 60 KC001782.1.1355_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_unidentified_marine_bacterioplankton
## 74 KC000519.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 43 AACY023868415.1.1427_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_marine_metagenome
## 91 KC002744.1.1344_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 102 KC002796.1.1323_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_2_unidentified_marine_bacterioplankton
## 48 KJ590614.1.1421_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_Sulfitobacter_uncultured_bacterium
## 85 KC001931.1.1353_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_unidentified_marine_bacterioplankton
## 62 JX945368.1.1448_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 77 KF786428.1.1342_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_SAR11_cluster_alpha_proteobacterium
## 8 DQ009267.1.1949_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_SAR116_clade_uncultured_marine_bacterium
## 7 JX945365.1.1423_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_uncultured_bacterium
## 42 HQ233040.1.1357_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 4 DQ009111.1.2063_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_marine_bacterium
## 51 KC000418.1.1315_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 63 KC002212.1.1315_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 21 KC002165.1.1315_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 88 GU061737.1.1446_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_uncultured_bacterium
## 2 KJ549180.1.1447_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Erythrobacteraceae_Erythrobacter_uncultured_bacterium
## 10 AACY020257759.244.1709_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_SAR116_clade_marine_metagenome
## 9 EU804112.1.1492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_uncultured_bacterium
## 6 JN986244.1.1428_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_uncultured_bacterium
## 5 EU237289.1.1306_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 75 EU802327.1.1487_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_uncultured_bacterium
## 28 EU802512.1.1493_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_bacterium
## 24 AACY023498084.1.1233_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_marine_metagenome
## 44 KC001705.1.1365_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_unidentified_marine_bacterioplankton
## 97 KC002188.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 40 EU804152.1.1492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_bacterium
## 16 KC001557.1.1292_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_unidentified_marine_bacterioplankton
## 13 EU237459.1.1302_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodospirillales_Rhodospirillaceae_AEGEAN-169_marine_group_uncultured_bacterium
## 84 KC001872.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_unidentified_marine_bacterioplankton
## 33 JN986006.1.1452_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_4_uncultured_bacterium
## 37 DQ009141.1.1996_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_uncultured_marine_bacterium
## 26 KC002130.1.1290_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_uncultured_unidentified_marine_bacterioplankton
## 29 HQ233043.1.1357_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 25 EU802705.1.1252_Bacteria_Bacteroidetes_Cytophagia_Cytophagales_Flammeovirgaceae_Marinoscillum_uncultured_bacterium
## 18 KJ549185.1.1449_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Erythrobacteraceae_Erythrobacter_uncultured_bacterium
## 39 FN433299.1.1479_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_Flavobacteriia_bacterium
## 22 DQ009121.1.1748_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_uncultured_marine_bacterium
## 66 KC002668.1.1343_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 53 KC002674.1.1343_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 72 JX945368.1.1448_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 98 JN985994.1.1438_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 34 EU802406.1.1257_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 90 FN433412.1.1496_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_Flavobacteriia_bacterium
## 35 AACY020563509.792.2305_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 78 KC002895.1.1345_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 52 AQSI01000003.54241.55782_Bacteria_Marinimicrobia__SAR406_clade__Marinimicrobia_bacterium_SCGC_AAA298-D23
## 93 EU804784.1.1433_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 99 KF786624.1.1388_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_Flavobacteriales_bacterium
## 38 JN986032.1.1449_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 11 AY664087.1.1207_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 47 JN986032.1.1449_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 20 KC003455.1.1350_Bacteria_Proteobacteria_Gammaproteobacteria_Alteromonadales_Alteromonadaceae_Alteromonas_unidentified_marine_bacterioplankton
## 71 EU805317.1.1450_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_SAR116_clade_uncultured_bacterium
## 31 EU804109.1.1485_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_NS9_marine_group_uncultured_bacterium
## 15 JNAU01000004.222174.223638_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_Prochlorococcus_sp._MIT_0601
## 45 KC000407.1.1363_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_Formosa_unidentified_marine_bacterioplankton
## 30 JX105591.1.1377_Bacteria_Actinobacteria_Actinobacteria_Corynebacteriales_Nocardiaceae_Rhodococcus_uncultured_bacterium
## 101 EU804476.1.1440_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 55 JN986342.1.1465_Bacteria_Proteobacteria_Alphaproteobacteria_Rickettsiales_S25-593_uncultured_bacterium
## 17 DQ396183.1.1451_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Erythrobacteraceae_Erythrobacter_uncultured_organism
## 65 AACY020549891.3846.5359_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 41 KC002791.1.1322_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplankton
## 12 JN166214.1.1446_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_marine_microorganism
## 50 EU804751.1.1482_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_NS7_marine_group_uncultured_bacterium
## 56 AACY020562322.3851.5364_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 54 EU803106.1.1287_Bacteria_Proteobacteria_Deltaproteobacteria_SAR324_clade_Marine_group_B__uncultured_bacterium
## 27 JQ516674.1.1506_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_uncultured_actinobacterium
## 64 EF572784.1.1439_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 49 KF786431.1.1388_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS2b_marine_group_uncultured_Flavobacteriales_bacterium
## 103 EU804974.1.1439_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 96 GQ346738.1.1322_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_alpha_proteobacterium
## 86 AACY020555764.489.1966_Bacteria_Proteobacteria_Alphaproteobacteria_OCS116_clade_marine_metagenome
## 61 JX945339.1.1492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_uncultured_bacterium
## 95 KM520431.1.1266_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 14 KC002188.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 70 KC294824.1.1401_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 87 AACY020462030.661.2167_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_marine_metagenome
## 3 JX526770.1.1401_Bacteria_Proteobacteria_Gammaproteobacteria_Thiotrichales_Thiotrichaceae_Thiothrix_uncultured_proteobacterium
## 67 AACY020481938.3418.4929_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_marine_metagenome
## 58 DQ009125.1.1942_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_SAR86_clade_uncultured_marine_bacterium
## 68 FQ032819.21712.23225_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS4_marine_group_uncultured_Flavobacteriia_bacterium
## 46 KC294823.1.1400_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 69 JN832945.1.1352_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_uncultured_bacterium
## 80 JQ013156.1.1423_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_Ascidiaceihabitans_uncultured_bacterium
## 76 AACY020490277.719.2228_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_marine_metagenome
## 59 KC811143.18830.20338_Bacteria_Actinobacteria_Acidimicrobiia_Acidimicrobiales_OM1_clade_Candidatus_Actinomarina_Candidatus_Actinomarina_minuta
## 81 DQ009089.1.1878_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_NS9_marine_group_uncultured_marine_bacterium
## 36 HQ622550.1.1449_Bacteria_Proteobacteria_Alphaproteobacteria_Rhizobiales_Aurantimonadaceae_Fulvimarina_Rhizobiales_bacterium_8047
## 83 ATUR01000005.1108.2578_Bacteria_Proteobacteria_Alphaproteobacteria_Sphingomonadales_Sphingomonadaceae_Sphingopyxis_Sphingopyxis_baekryungensis_DSM_16222
## 94 KC002188.1.1314_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Synechococcus_unidentified_marine_bacterioplankton
## 23 JQ032339.1.1400_Bacteria_Proteobacteria_Gammaproteobacteria_Oceanospirillales_Halomonadaceae_Halomonas_uncultured_bacterium
## 89 HQ233039.1.1357_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_Prochlorococcus_sp
## 57 JX945368.1.1448_Bacteria_Cyanobacteria_Cyanobacteria_SubsectionI_FamilyI_Prochlorococcus_uncultured_bacterium
## 73 EU795293.31983.33492_Bacteria_Bacteroidetes_Flavobacteriia_Flavobacteriales_Flavobacteriaceae_NS5_marine_group_uncultured_bacterium_HF0010_31F02
## 92 KC001532.1.1292_Bacteria_Proteobacteria_Alphaproteobacteria_Rhodobacterales_Rhodobacteraceae_Ruegeria_unidentified_marine_bacterioplankton
## 79 EU802825.1.1438_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_uncultured_bacterium
## 100 JN018663.1.1390_Bacteria_Proteobacteria_Gammaproteobacteria_KI89A_clade_uncultured_bacterium
## 82 KC003176.1.1343_Bacteria_Proteobacteria_Alphaproteobacteria_SAR11_clade_Surface_1_unidentified_marine_bacterioplanktonNumber and roportion of regionally abundant OTUs (%):
## [1] 103## [1] 1.260247
Cosmopolitan OTUs (relative abundance over 0.1% and occurence in more than 80% of samples):
## otu_names mean_rabund perc_occur SILVA_consensus PhytoREF_consensus
## 1 OTU_1 0.272434150 100.00000 <NA> <NA>
## 17 OTU_2 0.079469141 100.00000 <NA> <NA>
## 26 OTU_3 0.039445684 100.00000 <NA> <NA>
## 61 OTU_6 0.017176112 100.00000 <NA> <NA>
## 35 OTU_3619 0.017119533 100.00000 <NA> <NA>
## 72 OTU_8 0.013883869 100.00000 <NA> <NA>
## 82 OTU_9 0.010413234 100.00000 <NA> <NA>
## 69 OTU_7 0.009328911 100.00000 <NA> <NA>
## 52 OTU_51 0.007644182 100.00000 <NA> <NA>
## 64 OTU_62 0.006768869 100.00000 <NA> <NA>
## 7 OTU_14 0.006130522 100.00000 <NA> <NA>
## 6 OTU_13 0.005621309 100.00000 <NA> <NA>
## 3 OTU_11 0.005357051 100.00000 <NA> <NA>
## 53 OTU_52 0.005070162 100.00000 <NA> <NA>
## 8 OTU_15 0.003885327 100.00000 <NA> <NA>
## 36 OTU_38 0.003160447 100.00000 <NA> <NA>
## 27 OTU_30 0.002775709 100.00000 <NA> <NA>
## 28 OTU_303 0.002099421 100.00000 <NA> <NA>
## 65 OTU_6249 0.001911046 100.00000 <NA> <NA>
## 73 OTU_8015 0.001901061 100.00000 <NA> <NA>
## 58 OTU_57 0.001789899 100.00000 <NA> <NA>
## 81 OTU_8904 0.001714017 100.00000 <NA> <NA>
## 76 OTU_8415 0.001375873 100.00000 <NA> <NA>
## 37 OTU_396 0.001164866 100.00000 <NA> <NA>
## 63 OTU_61 0.001148890 100.00000 <NA> <NA>
## 42 OTU_43 0.005245224 98.90110 <NA> <NA>
## 24 OTU_2754 0.002637256 98.90110 <NA> <NA>
## 13 OTU_178 0.001763940 98.90110 <NA> <NA>
## 54 OTU_5214 0.001422468 98.90110 <NA> <NA>
## 57 OTU_5677 0.001268705 98.90110 <NA> <NA>
## 66 OTU_627 0.001025082 98.90110 <NA> <NA>
## 50 OTU_5 0.019958478 97.80220 <NA> <NA>
## 14 OTU_18 0.002965415 97.80220 <NA> <NA>
## 11 OTU_17 0.002817644 97.80220 <NA> <NA>
## 22 OTU_26 0.002640584 97.80220 <NA> <NA>
## 78 OTU_86 0.002167982 97.80220 <NA> <NA>
## 2 OTU_10 0.004287372 96.70330 <NA> <NA>
## 5 OTU_12 0.003390092 96.70330 <NA> <NA>
## 20 OTU_23 0.003173094 96.70330 <NA> <NA>
## 21 OTU_24 0.002628603 96.70330 <NA> <NA>
## 46 OTU_47 0.001692716 96.70330 <NA> <NA>
## 34 OTU_350 0.001594202 96.70330 <NA> <NA>
## 83 OTU_9607 0.001385192 96.70330 <NA> <NA>
## 16 OTU_19 0.002609965 95.60440 <NA> <NA>
## 15 OTU_182 0.001624821 95.60440 <NA> <NA>
## 70 OTU_72 0.001367885 95.60440 <NA> <NA>
## 75 OTU_836 0.001337932 95.60440 <NA> <NA>
## 9 OTU_16 0.004263409 94.50549 <NA> <NA>
## 43 OTU_44 0.001904389 94.50549 <NA> <NA>
## 25 OTU_29 0.001787237 94.50549 <NA> <NA>
## 32 OTU_34 0.002569361 93.40659 <NA> <NA>
## 33 OTU_35 0.003090555 91.20879 <NA> <NA>
## 44 OTU_45 0.002384314 91.20879 <NA> <NA>
## 29 OTU_31 0.002017548 91.20879 <NA> <NA>
## 79 OTU_8731 0.001886417 91.20879 <NA> <NA>
## 10 OTU_1666 0.001863785 91.20879 <NA> <NA>
## 56 OTU_54 0.001211460 91.20879 <NA> <NA>
## 60 OTU_59 0.001039060 91.20879 <NA> <NA>
## 71 OTU_75 0.004463766 90.10989 <NA> <NA>
## 48 OTU_4850 0.001060361 90.10989 <NA> <NA>
## 39 OTU_4 0.009755585 89.01099 <NA> <NA>
## 49 OTU_49 0.001192823 89.01099 <NA> <NA>
## 74 OTU_8316 0.001117606 89.01099 <NA> <NA>
## 55 OTU_5345 0.002511451 87.91209 <NA> <NA>
## 59 OTU_5713 0.002192610 87.91209 <NA> <NA>
## 19 OTU_22 0.001110284 87.91209 <NA> <NA>
## 67 OTU_6798 0.001002450 87.91209 <NA> <NA>
## 77 OTU_8518 0.003129162 86.81319 <NA> <NA>
## 38 OTU_3997 0.001828506 86.81319 <NA> <NA>
## 45 OTU_46 0.001455749 86.81319 <NA> <NA>
## 30 OTU_33 0.002741761 85.71429 <NA> <NA>
## 40 OTU_40 0.001399170 85.71429 <NA> <NA>
## 12 OTU_170 0.001290006 85.71429 <NA> <NA>
## 80 OTU_89 0.001016429 85.71429 <NA> <NA>
## 4 OTU_112 0.003313544 84.61538 <NA> <NA>
## 62 OTU_6052 0.003268946 84.61538 <NA> <NA>
## 23 OTU_27 0.003186407 83.51648 <NA> <NA>
## 41 OTU_42 0.001534960 83.51648 <NA> <NA>
## 68 OTU_6983 0.002793681 82.41758 <NA> <NA>
## 31 OTU_3305 0.001879095 82.41758 <NA> <NA>
## 47 OTU_48 0.001467065 82.41758 <NA> <NA>
## 51 OTU_5092 0.001357235 82.41758 <NA> <NA>
## 18 OTU_203 0.004783272 81.31868 <NA> <NA>Number and proportion (%) of cosmopolitan OTUs:
## [1] 83## [1] 1.015539Number and proportion (%) of rare OTUs:
## [1] 4665## [1] 57.078182.6) Taxonomic composition analysis
2.6.1) Normalized data
No. of OTUs and reads of the rearefied dataset:
## [1] 8173## [1] 1502319No. of OTUs and reads of phototrophic groups:
## [1] 1832## [1] 770960No. of OTUs and reads of non-phototrophic groups:
## [1] 6341## [1] 731359
PHOTOTROPHS + HETEROTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 128 11 31
## Bolidophyceae 81 4 41
## Chlorarachniophyceae 13 2 10
## Chlorodendrophyceae 2 1 2
## Cryptophyceae 40 4 18
## Cyanobacteria 752531 1211 91
## Dictyochophyceae 1333 31 88
## Dinophyceae 256 14 56
## Eustigmatophyceae 125 6 49
## Mamiellophyceae 860 16 26
## Pelagophyceae 399 19 65
## Prasinophyceae_clade-IX 578 18 73
## Prasinophyceae_clade-VII 293 15 34
## Prymnesiophyceae 8845 314 91
## Pyramimonadaceae 32 3 11
## Rappemonads 34 7 24
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 13 2 10
## other_bacteria 731359 6341 91
## other_plastids 5395 153 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.0000 100.0000 992.3077## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 8.520161e-03 0.13458950 34.065934
## Bolidophyceae 5.391664e-03 0.04894164 45.054945
## Chlorarachniophyceae 8.653289e-04 0.02447082 10.989011
## Chlorodendrophyceae 1.331275e-04 0.01223541 2.197802
## Cryptophyceae 2.662550e-03 0.04894164 19.780220
## Cyanobacteria 5.009129e+01 14.81708063 100.000000
## Dictyochophyceae 8.872949e-02 0.37929769 96.703297
## Dinophyceae 1.704032e-02 0.17129573 61.538462
## Eustigmatophyceae 8.320470e-03 0.07341246 53.846154
## Mamiellophyceae 5.724483e-02 0.19576655 28.571429
## Pelagophyceae 2.655894e-02 0.23247278 71.428571
## Prasinophyceae_clade-IX 3.847385e-02 0.22023737 80.219780
## Prasinophyceae_clade-VII 1.950318e-02 0.18353114 37.362637
## Prymnesiophyceae 5.887564e-01 3.84191851 100.000000
## Pyramimonadaceae 2.130040e-03 0.03670623 12.087912
## Rappemonads 2.263168e-03 0.08564786 26.373626
## Trebouxiophyceae 1.331275e-04 0.01223541 1.098901
## other_Prasinophyceae 8.653289e-04 0.02447082 10.989011
## other_bacteria 4.868200e+01 77.58473021 100.000000
## other_plastids 3.591115e-01 1.87201762 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
PHOTOTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 128 11 31
## Bolidophyceae 81 4 41
## Chlorarachniophyceae 13 2 10
## Chlorodendrophyceae 2 1 2
## Cryptophyceae 40 4 18
## Cyanobacteria 752531 1211 91
## Dictyochophyceae 1333 31 88
## Dinophyceae 256 14 56
## Eustigmatophyceae 125 6 49
## Mamiellophyceae 860 16 26
## Pelagophyceae 399 19 65
## Prasinophyceae_clade-IX 578 18 73
## Prasinophyceae_clade-VII 293 15 34
## Prymnesiophyceae 8845 314 91
## Pyramimonadaceae 32 3 11
## Rappemonads 34 7 24
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 13 2 10
## other_plastids 5395 153 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.0000 100.0000 892.3077## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 1.660268e-02 0.60043668 34.065934
## Bolidophyceae 1.050638e-02 0.21834061 45.054945
## Chlorarachniophyceae 1.686209e-03 0.10917031 10.989011
## Chlorodendrophyceae 2.594168e-04 0.05458515 2.197802
## Cryptophyceae 5.188337e-03 0.21834061 19.780220
## Cyanobacteria 9.760960e+01 66.10262009 100.000000
## Dictyochophyceae 1.729013e-01 1.69213974 96.703297
## Dinophyceae 3.320535e-02 0.76419214 61.538462
## Eustigmatophyceae 1.621355e-02 0.32751092 53.846154
## Mamiellophyceae 1.115492e-01 0.87336245 28.571429
## Pelagophyceae 5.175366e-02 1.03711790 71.428571
## Prasinophyceae_clade-IX 7.497146e-02 0.98253275 80.219780
## Prasinophyceae_clade-VII 3.800457e-02 0.81877729 37.362637
## Prymnesiophyceae 1.147271e+00 17.13973799 100.000000
## Pyramimonadaceae 4.150669e-03 0.16375546 12.087912
## Rappemonads 4.410086e-03 0.38209607 26.373626
## Trebouxiophyceae 2.594168e-04 0.05458515 1.098901
## other_Prasinophyceae 1.686209e-03 0.10917031 10.989011
## other_plastids 6.997769e-01 8.35152838 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
Absolute values of cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 688078 979
## Synechococcus 61311 162
## other_cyanobacteria 3142 70Relative values of Cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 91.4351701 80.842279
## Synechococcus 8.1473056 13.377374
## Other cyanobacteria 0.4175243 5.780347
PROTISTS
## [1] 621## [1] 18429occurrence_counts_phototrophs<-data.table()
nrow(tb16_phototrophs)## [1] 621#create a table per group and count in how many samples they occur.
Dinophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Dinophyceae"),]
Dinophyceae_tb_occur <- Dinophyceae_tb[,1:91]
Dinophyceae_tb_occur_len<-length(Dinophyceae_tb_occur[,colSums(Dinophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Dinophyceae",samples_per_class=Dinophyceae_tb_occur_len))
Prasinophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "other_Prasinophyceae"),]
Prasinophyceae_tb_occur <- Prasinophyceae_tb[,1:91]
Prasinophyceae_tb_occur_len<-length(Prasinophyceae_tb_occur[,colSums(Prasinophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="other_Prasinophyceae",samples_per_class=Prasinophyceae_tb_occur_len))
Chrysophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chrysophyceae"),]
Chrysophyceae_tb_occur <- Chrysophyceae_tb[,1:91]
Chrysophyceae_tb_occur_len<-length(Chrysophyceae_tb_occur[,colSums(Chrysophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chrysophyceae",samples_per_class=Chrysophyceae_tb_occur_len))
Pelagophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pelagophyceae"),]
Pelagophyceae_tb_occur <- Pelagophyceae_tb[,1:91]
Pelagophyceae_tb_occur_len<-length(Pelagophyceae_tb_occur[,colSums(Pelagophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pelagophyceae",samples_per_class=Pelagophyceae_tb_occur_len))
Dictyochophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Dictyochophyceae"),]
Dictyochophyceae_tb_occur <- Dictyochophyceae_tb[,1:91]
Dictyochophyceae_tb_occur_len<-length(Dictyochophyceae_tb_occur[,colSums(Dictyochophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Dictyochophyceae",samples_per_class=Dictyochophyceae_tb_occur_len))
Cryptophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Cryptophyceae"),]
Cryptophyceae_tb_occur <- Cryptophyceae_tb[,1:91]
Cryptophyceae_tb_occur_len<-length(Cryptophyceae_tb_occur[,colSums(Cryptophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Cryptophyceae",samples_per_class=Cryptophyceae_tb_occur_len))
Bacillariophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Bacillariophyceae"),]
Bacillariophyceae_tb_occur <- Bacillariophyceae_tb[,1:91]
Bacillariophyceae_tb_occur_len<-length(Bacillariophyceae_tb_occur[,colSums(Bacillariophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Bacillariophyceae",samples_per_class=Bacillariophyceae_tb_occur_len))
Chlorarachniophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chlorarachniophyceae"),]
Chlorarachniophyceae_tb_occur <- Chlorarachniophyceae_tb[,1:91]
Chlorarachniophyceae_tb_occur_len<-length(Chlorarachniophyceae_tb_occur[,colSums(Chlorarachniophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chlorarachniophyceae",samples_per_class=Chlorarachniophyceae_tb_occur_len))
Bolidophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Bolidophyceae"),]
Bolidophyceae_tb_occur <- Bolidophyceae_tb[,1:91]
Bolidophyceae_tb_occur_len<-length(Bolidophyceae_tb_occur[,colSums(Bolidophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Bolidophyceae",samples_per_class=Bolidophyceae_tb_occur_len))
Pinguiophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pinguiophyceae"),]
Pinguiophyceae_tb_occur <- Pinguiophyceae_tb[,1:91]
Pinguiophyceae_tb_occur_len<-length(Pinguiophyceae_tb_occur[,colSums(Pinguiophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pinguiophyceae",samples_per_class=Pinguiophyceae_tb_occur_len))
Prymnesiophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Prymnesiophyceae"),]
Prymnesiophyceae_tb_occur <- Prymnesiophyceae_tb[,1:91]
Prymnesiophyceae_tb_occur_len<-length(Prymnesiophyceae_tb_occur[,colSums(Prymnesiophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Prymnesiophyceae",samples_per_class=Prymnesiophyceae_tb_occur_len))
Mamiellophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Mamiellophyceae"),]
Mamiellophyceae_tb_occur <- Mamiellophyceae_tb[,1:91]
Mamiellophyceae_tb_occur_len<-length(Mamiellophyceae_tb_occur[,colSums(Mamiellophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Mamiellophyceae",samples_per_class=Mamiellophyceae_tb_occur_len))
Eustigmatophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Eustigmatophyceae"),]
Eustigmatophyceae_tb_occur <- Eustigmatophyceae_tb[,1:91]
Eustigmatophyceae_tb_occur_len<-length(Eustigmatophyceae_tb_occur[,colSums(Eustigmatophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Eustigmatophyceae",samples_per_class=Eustigmatophyceae_tb_occur_len))
Chlorophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chlorophyceae"),]
Chlorophyceae_tb_occur <- Chlorophyceae_tb[,1:91]
Chlorophyceae_tb_occur_len<-length(Chlorophyceae_tb_occur[,colSums(Chlorophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chlorophyceae",samples_per_class=Chlorophyceae_tb_occur_len))
Ulvophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Ulvophyceae"),]
Ulvophyceae_tb_occur <- Ulvophyceae_tb[,1:91]
Ulvophyceae_tb_occur_len<-length(Ulvophyceae_tb_occur[,colSums(Ulvophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Ulvophyceae",samples_per_class=Ulvophyceae_tb_occur_len))
Raphydophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Raphydophyceae"),]
Raphydophyceae_tb_occur <- Raphydophyceae_tb[,1:91]
Raphydophyceae_tb_occur_len<-length(Raphydophyceae_tb_occur[,colSums(Raphydophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Raphydophyceae",samples_per_class=Raphydophyceae_tb_occur_len))
Trebouxiophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Trebouxiophyceae"),]
Trebouxiophyceae_tb_occur <- Trebouxiophyceae_tb[,1:91]
Trebouxiophyceae_tb_occur_len<-length(Trebouxiophyceae_tb_occur[,colSums(Trebouxiophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Trebouxiophyceae",samples_per_class=Trebouxiophyceae_tb_occur_len))
Phaeophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Phaeophyceae"),]
Phaeophyceae_tb_occur <- Phaeophyceae_tb[,1:91]
Phaeophyceae_tb_occur_len<-length(Phaeophyceae_tb_occur[,colSums(Phaeophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Phaeophyceae",samples_per_class=Phaeophyceae_tb_occur_len))
Phaeothamniophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Phaeothamniophyceae"),]
Phaeothamniophyceae_tb_occur <- Phaeothamniophyceae_tb[,1:91]
Phaeothamniophyceae_tb_occur_len<-length(Phaeothamniophyceae_tb_occur[,colSums(Phaeothamniophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Phaeothamniophyceae",samples_per_class=Phaeothamniophyceae_tb_occur_len))
Xanthophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Xanthophyceae"),]
Xanthophyceae_tb_occur <- Xanthophyceae_tb[,1:91]
Xanthophyceae_tb_occur_len<-length(Xanthophyceae_tb_occur[,colSums(Xanthophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Xanthophyceae",samples_per_class=Xanthophyceae_tb_occur_len))
Chlorodendrophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Chlorodendrophyceae"),]
Chlorodendrophyceae_tb_occur <- Chlorodendrophyceae_tb[,1:91]
Chlorodendrophyceae_tb_occur_len<-length(Chlorodendrophyceae_tb_occur[,colSums(Chlorodendrophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Chlorodendrophyceae",samples_per_class=Chlorodendrophyceae_tb_occur_len))
IncertaeSedis_Archaeplastida_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "IncertaeSedis_Archaeplastida"),]
IncertaeSedis_Archaeplastida_tb_occur <- IncertaeSedis_Archaeplastida_tb[,1:91]
IncertaeSedis_Archaeplastida_tb_occur_len<-length(IncertaeSedis_Archaeplastida_tb_occur[,colSums(IncertaeSedis_Archaeplastida_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="IncertaeSedis_Archaeplastida",samples_per_class=IncertaeSedis_Archaeplastida_tb_occur_len))
Nephroselmidophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Nephroselmidophyceae"),]
Nephroselmidophyceae_tb_occur <- Nephroselmidophyceae_tb[,1:91]
Nephroselmidophyceae_tb_occur_len<-length(Nephroselmidophyceae_tb_occur[,colSums(Nephroselmidophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Nephroselmidophyceae",samples_per_class=Nephroselmidophyceae_tb_occur_len))
Pavlovophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pavlovophyceae"),]
Pavlovophyceae_tb_occur <- Pavlovophyceae_tb[,1:91]
Pavlovophyceae_tb_occur_len<-length(Pavlovophyceae_tb_occur[,colSums(Pavlovophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pavlovophyceae",samples_per_class=Pavlovophyceae_tb_occur_len))
Rhodophyceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Rhodophyceae"),]
Rhodophyceae_tb_occur <- Rhodophyceae_tb[,1:91]
Rhodophyceae_tb_occur_len<-length(Rhodophyceae_tb_occur[,colSums(Rhodophyceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Rhodophyceae",samples_per_class=Rhodophyceae_tb_occur_len))
Rappemonads_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Rappemonads"),]
Rappemonads_tb_occur <- Rappemonads_tb[,1:91]
Rappemonads_tb_occur_len<-length(Rappemonads_tb_occur[,colSums(Rappemonads_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Rappemonads",samples_per_class=Rappemonads_tb_occur_len))
MOCH_1_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "MOCH_1"),]
MOCH_1_tb_occur <- MOCH_1_tb[,1:91]
MOCH_1_tb_occur_len<-length(MOCH_1_tb_occur[,colSums(MOCH_1_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="MOCH_1",samples_per_class=MOCH_1_tb_occur_len))
MOCH_2_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "MOCH_2"),]
MOCH_2_tb_occur <- MOCH_2_tb[,1:91]
MOCH_2_tb_occur_len<-length(MOCH_2_tb_occur[,colSums(MOCH_2_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="MOCH_2",samples_per_class=MOCH_2_tb_occur_len))
MOCH_5_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "MOCH_5"),]
MOCH_5_tb_occur <- MOCH_5_tb[,1:91]
MOCH_5_tb_occur_len<-length(MOCH_5_tb_occur[,colSums(MOCH_5_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="MOCH_5",samples_per_class=MOCH_5_tb_occur_len))
Prasinophyceae_clade_VII_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Prasinophyceae_clade-VII"),]
Prasinophyceae_clade_VII_tb_occur <- Prasinophyceae_clade_VII_tb[,1:91]
Prasinophyceae_clade_VII_tb_occur_len<-length(Prasinophyceae_clade_VII_tb_occur[,colSums(Prasinophyceae_clade_VII_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Prasinophyceae_clade-VII",samples_per_class=Prasinophyceae_clade_VII_tb_occur_len))
Prasinophyceae_clade_IX_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Prasinophyceae_clade-IX"),]
Prasinophyceae_clade_IX_tb_occur <- Prasinophyceae_clade_IX_tb[,1:91]
Prasinophyceae_clade_IX_tb_occur_len<-length(Prasinophyceae_clade_IX_tb_occur[,colSums(Prasinophyceae_clade_IX_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Prasinophyceae_clade-IX",samples_per_class=Prasinophyceae_clade_IX_tb_occur_len))
Pyramimonadaceae_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "Pyramimonadaceae"),]
Pyramimonadaceae_tb_occur <- Pyramimonadaceae_tb[,1:91]
Pyramimonadaceae_tb_occur_len<-length(Pyramimonadaceae_tb_occur[,colSums(Pyramimonadaceae_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="Pyramimonadaceae",samples_per_class=Pyramimonadaceae_tb_occur_len))
other_plastids_tb <- tb16_phototrophs[which(tb16_phototrophs$class_A == "other_plastids"),]
other_plastids_tb_occur <- other_plastids_tb[,1:91]
other_plastids_tb_occur_len<-length(other_plastids_tb_occur[,colSums(other_plastids_tb_occur) > 0])
occurrence_counts_phototrophs<-rbind(occurrence_counts_phototrophs,data.table(group="other_plastids",samples_per_class=other_plastids_tb_occur_len))
occurrence_counts_phototrophs## group samples_per_class
## 1: Dinophyceae 56
## 2: other_Prasinophyceae 10
## 3: Chrysophyceae 0
## 4: Pelagophyceae 65
## 5: Dictyochophyceae 88
## 6: Cryptophyceae 18
## 7: Bacillariophyceae 31
## 8: Chlorarachniophyceae 10
## 9: Bolidophyceae 41
## 10: Pinguiophyceae 0
## 11: Prymnesiophyceae 91
## 12: Mamiellophyceae 26
## 13: Eustigmatophyceae 49
## 14: Chlorophyceae 0
## 15: Ulvophyceae 0
## 16: Raphydophyceae 0
## 17: Trebouxiophyceae 1
## 18: Phaeophyceae 0
## 19: Phaeothamniophyceae 0
## 20: Xanthophyceae 0
## 21: Chlorodendrophyceae 2
## 22: IncertaeSedis_Archaeplastida 0
## 23: Nephroselmidophyceae 0
## 24: Pavlovophyceae 0
## 25: Rhodophyceae 0
## 26: Rappemonads 24
## 27: MOCH_1 0
## 28: MOCH_2 0
## 29: MOCH_5 0
## 30: Prasinophyceae_clade-VII 34
## 31: Prasinophyceae_clade-IX 73
## 32: Pyramimonadaceae 11
## 33: other_plastids 91
## group samples_per_class#row.names(occurrence_counts_phototrophs)<-occurrence_counts_phototrophs$group
occurrence_counts_phototrophs<-as.data.frame(occurrence_counts_phototrophs)Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 128 11 31
## Bolidophyceae 81 4 41
## Chlorarachniophyceae 13 2 10
## Chlorodendrophyceae 2 1 2
## Cryptophyceae 40 4 18
## Dictyochophyceae 1333 31 88
## Dinophyceae 256 14 56
## Eustigmatophyceae 125 6 49
## Mamiellophyceae 860 16 26
## Pelagophyceae 399 19 65
## Prasinophyceae_clade-IX 578 18 73
## Prasinophyceae_clade-VII 293 15 34
## Prymnesiophyceae 8845 314 91
## Pyramimonadaceae 32 3 11
## Rappemonads 34 7 24
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 13 2 10
## other_plastids 5395 153 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.0000 100.0000 792.3077## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 0.69455749 1.7713366 34.065934
## Bolidophyceae 0.43952466 0.6441224 45.054945
## Chlorarachniophyceae 0.07054100 0.3220612 10.989011
## Chlorodendrophyceae 0.01085246 0.1610306 2.197802
## Cryptophyceae 0.21704922 0.6441224 19.780220
## Dictyochophyceae 7.23316512 4.9919485 96.703297
## Dinophyceae 1.38911498 2.2544283 61.538462
## Eustigmatophyceae 0.67827880 0.9661836 53.846154
## Mamiellophyceae 4.66655814 2.5764895 28.571429
## Pelagophyceae 2.16506593 3.0595813 71.428571
## Prasinophyceae_clade-IX 3.13636117 2.8985507 80.219780
## Prasinophyceae_clade-VII 1.58988551 2.4154589 37.362637
## Prymnesiophyceae 47.99500787 50.5636071 100.000000
## Pyramimonadaceae 0.17363937 0.4830918 12.087912
## Rappemonads 0.18449183 1.1272142 26.373626
## Trebouxiophyceae 0.01085246 0.1610306 1.098901
## other_Prasinophyceae 0.07054100 0.3220612 10.989011
## other_plastids 29.27451300 24.6376812 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
2.6.2) Non-rarefied data
No. of OTUs and reads of the rearefied dataset:
## [1] 8881## [1] 4745946No. of OTUs and reads of phototrophic groups:
## [1] 1952## [1] 2504586No. of OTUs and reads of non-phototrophic groups:
## [1] 6929## [1] 2241360
PHOTOTROPHS + HETEROTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 450 14 42
## Bolidophyceae 271 4 61
## Chlorarachniophyceae 25 2 16
## Chlorodendrophyceae 8 1 5
## Cryptophyceae 103 6 24
## Cyanobacteria 2449889 1278 91
## Dictyochophyceae 4003 31 89
## Dinophyceae 819 17 73
## Eustigmatophyceae 369 6 56
## Mamiellophyceae 1248 16 34
## Pelagophyceae 1076 19 75
## Prasinophyceae_clade-IX 1785 23 80
## Prasinophyceae_clade-VII 923 16 49
## Prymnesiophyceae 26832 336 91
## Pyramimonadaceae 51 3 17
## Rappemonads 136 7 41
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 27 2 16
## other_bacteria 2241360 6929 91
## other_plastids 16569 170 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.000 100.000 1146.154## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 9.481777e-03 0.15763991 46.153846
## Bolidophyceae 5.710137e-03 0.04503997 67.032967
## Chlorarachniophyceae 5.267654e-04 0.02251999 17.582418
## Chlorodendrophyceae 1.685649e-04 0.01125999 5.494505
## Cryptophyceae 2.170273e-03 0.06755996 26.373626
## Cyanobacteria 5.162067e+01 14.39027137 100.000000
## Dictyochophyceae 8.434567e-02 0.34905979 97.802198
## Dinophyceae 1.725683e-02 0.19141989 80.219780
## Eustigmatophyceae 7.775057e-03 0.06755996 61.538462
## Mamiellophyceae 2.629613e-02 0.18015989 37.362637
## Pelagophyceae 2.267198e-02 0.21393987 82.417582
## Prasinophyceae_clade-IX 3.761105e-02 0.25897984 87.912088
## Prasinophyceae_clade-VII 1.944818e-02 0.18015989 53.846154
## Prymnesiophyceae 5.653667e-01 3.78335773 100.000000
## Pyramimonadaceae 1.074601e-03 0.03377998 18.681319
## Rappemonads 2.865604e-03 0.07881995 45.054945
## Trebouxiophyceae 4.214123e-05 0.01125999 1.098901
## other_Prasinophyceae 5.689066e-04 0.02251999 17.582418
## other_bacteria 4.722683e+01 78.02049319 100.000000
## other_plastids 3.491190e-01 1.91419885 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
PHOTOTROPHS
Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 450 14 42
## Bolidophyceae 271 4 61
## Chlorarachniophyceae 25 2 16
## Chlorodendrophyceae 8 1 5
## Cryptophyceae 103 6 24
## Cyanobacteria 2449889 1278 91
## Dictyochophyceae 4003 31 89
## Dinophyceae 819 17 73
## Eustigmatophyceae 369 6 56
## Mamiellophyceae 1248 16 34
## Pelagophyceae 1076 19 75
## Prasinophyceae_clade-IX 1785 23 80
## Prasinophyceae_clade-VII 923 16 49
## Prymnesiophyceae 26832 336 91
## Pyramimonadaceae 51 3 17
## Rappemonads 136 7 41
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 27 2 16
## other_plastids 16569 170 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.000 100.000 1046.154## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 1.796704e-02 0.71721311 46.153846
## Bolidophyceae 1.082015e-02 0.20491803 67.032967
## Chlorarachniophyceae 9.981690e-04 0.10245902 17.582418
## Chlorodendrophyceae 3.194141e-04 0.05122951 5.494505
## Cryptophyceae 4.112456e-03 0.30737705 26.373626
## Cyanobacteria 9.781613e+01 65.47131148 100.000000
## Dictyochophyceae 1.598268e-01 1.58811475 97.802198
## Dinophyceae 3.270002e-02 0.87090164 80.219780
## Eustigmatophyceae 1.473297e-02 0.30737705 61.538462
## Mamiellophyceae 4.982859e-02 0.81967213 37.362637
## Pelagophyceae 4.296119e-02 0.97336066 82.417582
## Prasinophyceae_clade-IX 7.126926e-02 1.17827869 87.912088
## Prasinophyceae_clade-VII 3.685240e-02 0.81967213 53.846154
## Prymnesiophyceae 1.071315e+00 17.21311475 100.000000
## Pyramimonadaceae 2.036265e-03 0.15368852 18.681319
## Rappemonads 5.430039e-03 0.35860656 45.054945
## Trebouxiophyceae 7.985352e-05 0.05122951 1.098901
## other_Prasinophyceae 1.078022e-03 0.10245902 17.582418
## other_plastids 6.615465e-01 8.70901639 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
Absolute values of cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 2287847 1036
## Synechococcus 152047 167
## other_cyanobacteria 9995 75Relative values of Cyanobacteria groups richness and abundance:
## reads_per_class OTUs_per_class
## Prochlorococcus 93.3857411 81.064163
## Synechococcus 6.2062812 13.067293
## Other cyanobacteria 0.4079777 5.868545
PROTISTS
## [1] 674## [1] 54697Absolute values
## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 450 14 42
## Bolidophyceae 271 4 61
## Chlorarachniophyceae 25 2 16
## Chlorodendrophyceae 8 1 5
## Cryptophyceae 103 6 24
## Dictyochophyceae 4003 31 89
## Dinophyceae 819 17 73
## Eustigmatophyceae 369 6 56
## Mamiellophyceae 1248 16 34
## Pelagophyceae 1076 19 75
## Prasinophyceae_clade-IX 1785 23 80
## Prasinophyceae_clade-VII 923 16 49
## Prymnesiophyceae 26832 336 91
## Pyramimonadaceae 51 3 17
## Rappemonads 136 7 41
## Trebouxiophyceae 2 1 1
## other_Prasinophyceae 27 2 16
## other_plastids 16569 170 91
Relative values
## reads_per_class OTUs_per_class samples_per_class
## 100.0000 100.0000 946.1538## reads_per_class OTUs_per_class samples_per_class
## Bacillariophyceae 0.822714226 2.0771513 46.153846
## Bolidophyceae 0.495456789 0.5934718 67.032967
## Chlorarachniophyceae 0.045706346 0.2967359 17.582418
## Chlorodendrophyceae 0.014626031 0.1483680 5.494505
## Cryptophyceae 0.188310145 0.8902077 26.373626
## Dictyochophyceae 7.318500101 4.5994065 97.802198
## Dinophyceae 1.497339891 2.5222552 80.219780
## Eustigmatophyceae 0.674625665 0.8902077 61.538462
## Mamiellophyceae 2.281660786 2.3738872 37.362637
## Pelagophyceae 1.967201126 2.8189911 82.417582
## Prasinophyceae_clade-IX 3.263433095 3.4124629 87.912088
## Prasinophyceae_clade-VII 1.687478289 2.3738872 53.846154
## Prymnesiophyceae 49.055706894 49.8516320 100.000000
## Pyramimonadaceae 0.093240946 0.4451039 18.681319
## Rappemonads 0.248642522 1.0385757 45.054945
## Trebouxiophyceae 0.003656508 0.1483680 1.098901
## other_Prasinophyceae 0.049362854 0.2967359 17.582418
## other_plastids 30.292337788 25.2225519 100.000000
Reads per class vs. OTUs per class:
Reads per class vs. samples in which they occurr:
